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Research on the No Flow in the Lower Yellow River Based on Matlab

Author: JiShuHua
Tutor: ZhaoQuanSheng
School: Qingdao University
Course: Environmental Science
Keywords: Lower Yellow River No flow BP Artificial Neural Network MATLAB
CLC: TV882.1
Type: Master's thesis
Year: 2006
Downloads: 133
Quote: 2
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Abstract


During the past forty years, with the increase of population and the development of industry and agriculture, the water resource was lack increasingly in the area of Yellow River, especially in the Lower Yellow River. The lack of water resource has made them unbalance among population、 resource and environmental systems. It was the fist no flow in 1972. From 1972 to 1997, the no flow appears in twenty years, the days of no flow add up to 908 days, and the average is 45. 4 days in every year.BP Neural Networks are many layers Networks containing input, conceal and output layers. If this Neural Network is regarded as a mapping from input to output, this mapping is a highly nonlinear mapping. BP Algorithm may resolve the complicated mapping by complex of the simple nonlinear function. It is proved that a three-layer BP neural network to forecast runoff in the Lower Yellow River can better depict the model’ s feature of complex nonlinear、 multi-input-output and indefinite.By using the Neural Network Toolbox(NNT) of MATLAB, BP Artificial Neural Network Models are set up to predict the dry season Runoff in the Lower Yellow River. Through the thesis, we can see that it is easy and effective by using the Neural Network Toolbox (NNT) based on BP Models to forecast runoff in the Lower Yellow River.By analyzing the influence factors of dry season runoff in the Lower Yellow River, HuaYuanKou station runoff discharge and supplying water are influence factors. These two factors are as input layer, LiJin station runoff discharge is output layer. Based on the influence factors analyses, BP Models of dry season runoff forecast of the Lower Yellow River are set up. By using the Artificial Neural Network theory and the Neural Network Toolbox(NNT) of MATLAB, BP Artificial Neural Network Models are set up to predict the dry season Runoff in the Lower Yellow River. BP Models are trained and tested using data of 28 years of HuaYuanKou and Lijin stations. At the same time, another Model is set up to forecast the data of Lijin station from 2000 to 2010 by using data of 28 years of Lijin.The results show that the BP models are reasonable and reliable. It reflects the runoff law in the Lower Yellow River. Decision departments may input many plans to models to choose the best plan according to calculation results. BP Models may provide the scientific basis for the Yellow River basin water resources integrated management and administration;especially prevent the no flow from appearing again.

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CLC: > Industrial Technology > Hydraulic Engineering > River training works and flood control projects > Countries in the world of river management > China > Yellow River
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